One objective of a manufacturing process is to convert raw materials into desired products in the presence of ever-changing external influences such as air temperature, water temperature, etc. and requirements such as product specifications, operational constraints and safety and environmental regulations. The primary incentive for using advanced process control (APC) and real time optimization (RTO) is to steer system operation in regions that maximize profits in the presence of constraints.
In current practice, this incentive is pursued using a remote set-point/target passing framework between an RTO module and an APC module. This remote set-point passing strategy between an RTO module and an APC module is essentially a translation of the main economic objective into a process control objective. For example, in a conventional process automation scheme, an RTO module attempts to drive an APC module to the most economic operating point by communicating targets for a set of process control variables (e.g. all process control variables or a subset of process control variables). Specifically, the RTO module generally contains a rigorous, nonlinear, steady-state model of the economics and constraints of the process to be controlled. When the process is in a steady-state (i.e. a state in which a set of defined variables are within a prescribed tolerance of variability), the RTO module uses this model to calculate an economically optimum operating point which is communicated to the APC module in the form of targets for specific process control variables. The APC module, executing at a much higher frequency than the RTO module, attempts to dynamically drive the process variables towards their respective targets while honoring all constraints. The APC module continues to drive the process variables towards their respective targets until new targets are received from the RTO system.
In the conventional approach to coordinating operations between an RTO module and an APC module, as described above, the targets passed by the RTO module to the APC module represent a constrained economic optimum at the time when the RTO module pulled process, constraint and economic data. This constrained economic optimum represents the optimum according to constraints applied to various advance process control variables in the process by operators, engineers, or others. However, the APC module has no knowledge of the unconstrained economic optimum, which represents the optimum without consideration of constraints.
FIG. 1a illustrates the locations of unconstrained economic optimum 102 and constrained economic optimum 104 in graph 100 of the process control variables TG1 and TG2 which relate to a manufacturing process. Each circle in contour 103 represents constant value of the economic objective function used by the RTO module. Contour 105 represents the APC objective function which minimizes the distance from constrained economic optimum 104, where distance is measured as a weighted sum of the squared differences between the values of the variables and their targets. For example, TG1 may relate to the flow rate of a process stream in a production process that might be utilized in a petrochemical plant and TG2 may relate to the temperature of a stream. The manufacturing process represented in graph 100, is controlled by a process control and optimization system of the prior art which includes a RTO module and an APC module working in conjunction to drive the manufacturing process towards an economic optimum which honors all constraints. Constraint 106 limits the feasible values for process control variable TG1. Adhering to constraint 106, the RTO module will determine constrained economic optimum 104 for the manufacturing process which is comprised of a specific value for each of TG1 and TG2. For example, in the situation described above in which TG1 represents the feed flow rate of a substance at the inlet of a reactor vessel performing the manufacturing process, constraint 106 may be set by an operator of the manufacturing process to a maximum of ten cubic meters per second. Thus, based on constraint 106, the RTO module will transmit a target for TG1 to the APC module that is less than or equal to ten cubic meters per second along with a target for TG2. For example, the RTO module may send to the APC module a TG1 target of ten cubic meters per second and a TG2 target of 120 degree Celsius. These targets represent constrained economic optimum 104 at which the manufacturing process is most profitable while adhering to all constraints (i.e. constraint 106). The APC module in turn drives feed flow rate TG1 toward the ten cubic meters per second and temperature TG2 toward 120 degrees Celsius. Although there are values for TG1 and TG2 which provide greater profitability for the manufacturing process (e.g. unconstrained economic optimum 102), these values for TG1 and TG2 do not honor constraint 106. Thus, steering the manufacturing process towards constrained economic optimum 104 is ideal while constraint 106 remains at the same location in graph 100.
Upon the occurrence of a disturbance which moves constrained economic optimum 104, the targets set for TG1 and TG2 by the RTO module are no longer optimal. For example, a disturbance may occur which changes constraint 106 such that more “room” is provided for TG1. For example, an operator may change constraint 106 to twelve cubic meters per second.
FIG. 1b illustrates graph 100 upon the occurrence of the disturbance described above. Unconstrained economic optimum 102 remains in the same location as before while new constrained economic optimum 108 has been created based on the disturbance. New constrained economic optimum 108 is closer to unconstrained economic optimum 102 than constrained economic optimum 104, because the change in constraint 106 allows new constrained economic optimum 108 to take advantage of the greater room for TG1. Under the traditional set-point process control system, the APC module would be unaware of the new constrained economic optimum 108 until the RTO submitted new set-point targets which corresponded to new constrained economic optimum 108.
Alternatively, a disturbance may occur which changes constraint 106 such that less “room” is provided for TG1, making the targets set for TG1 and TG2 by the RTO module no longer feasible. For example, an operator may change constraint 106 to eight cubic meters per second.
FIG. 1c illustrates graph 100 upon the occurrence of the disturbance described above. Unconstrained economic optimum 102 remains in the same location as before while new constrained economic optimum 110 has been created based on the disturbance. New constrained economic optimum 110 is further from unconstrained economic optimum 102 than constrained economic optimum 104, because the change in constraint 106 makes constrained optimum 104 infeasible. Under the traditional set-point process control system, the APC module would attempt to find a feasible point that minimized the distance from constrained economic optimum 104, based on its non-economic objective function. In this example, that point is APC target 112 which is different from the new constrained economic optimum 110.
However, since RTO modules traditionally run infrequently in comparison to APC modules, the APC module will drive towards the old set-point targets (i.e. constrained economic optimum 104) for an extended period of time while it awaits a new set of set-point targets (i.e. new constrained economic optimum 108) which either takes advantage of the greater room for operation or economically adjusts for a reduction in the feasible region resulting from a change to constraint 106. Potentially a great sum of profit will be lost between when constraint 106 is changed and when the APC module receives a new set of set-point targets.
Therefore, according to conventional approaches for coordinating operations between an RTO module and an APC module, as described above, when disturbances enter the process (e.g. ambient temperature variation, operator limits changes, etc.), the APC module continues to try to hold the process at the target values originally set by the RTO module. These disturbances may change the constraints relative to the targets, either by relaxing active constraints or making the targets infeasible. In the first case, the APC module does not take advantage of the extra “room” within the feasible region because the APC module is unaware of the extra “room” precipitated by the relaxation of the active constraints. In the second case, the APC module gets as close as possible to the targets using non-economic criteria and arrives at possibly a non-optimum economic point. In either case the conventional APC module's strategy does not seek to optimize profit.
Further, as mentioned above, RTO modules traditionally run relatively infrequently with respect to APC modules. One example is an RTO module running hourly compared to an APC module running every minute. RTO solutions lag behind changes in the process because of RTO modules' infrequent operation. The time between when a disturbance enters the process and new targets from the RTO module that account for the disturbance is passed to APC modules represents lost opportunity. The more significant the disturbance (in magnitude and duration), the more likely the time between receiving updated targets from the RTO module will increase, since the RTO module requires the process to be in a steady-state to calculate new targets.
Technology available today for linking APC modules and RTO modules utilize a remote set-point passing strategy. Translation of objectives in this fashion results in a loss of economic information as the APC module has no information about the original process economics and is vulnerable to disturbances to the process or changes to operating constraints.
Thus, there is a desire for a system and method for coordinating execution of a RTO module with an APC module to more closely approach optimal economic operation of a manufacturing process.